Machine learning in solar physics

The application of machine learning in solar physics has the potential to greatly enhance our understanding of the complex processes that take place in the atmosphere of the Sun. By using techniques such as deep learning, we are now in the position to analyze large amounts of data from solar observations and identify patterns and trends that may not have been apparent using traditional methods. This can help us improve our understanding of explosive events like solar flares, which can have a strong effect on the Earth environment. Predicting hazardous events on Earth becomes crucial for our technological society. Machine learning can also improve our understanding of the inner workings of the sun itself by allowing us to go deeper into the data and to propose more complex models to explain them. Additionally, the use of machine learning can help to automate the analysis of solar data, reducing the need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a Living Review in Solar Physics (LRSP

Differential Gene Expression Profiles Reflecting Macrophage Polarization in Aging and Periodontitis Gingival Tissues

Recent evidence has determined a phenotypic and functional heterogeneity for macrophage populations. This plasticity of macrophage function has been related to specific properties of subsets (M1 and M2) of these cells in inflammation, adaptive immune responses and resolution of tissue destructive processes. This investigation hypothesized that targeted alterations in the distribution of macrophage phenotypes in aged individuals, and with periodontitis would be skewed towards M1 inflammatory macrophages in gingival tissues. The study used a non-human primate model to evaluate gene expression profiles as footprints of macrophage variation in healthy and periodontitis gingival tissues from animals 3-23 years of age and in periodontitis tissues in adult and aged animals. Significant increases in multiple genes reflecting overall increases in macrophage activities were observed in healthy aged tissues, and were significantly increased in periodontitis tissues from both adults and aged animals. Generally, gene expression patterns for M2 macrophages were similar in healthy young, adolescent and adult tissues. However, modest increases were noted in healthy aged tissues, similar to those seen in periodontitis tissues from both age groups. M1 macrophage gene transcription patterns increased significantly over the age range in healthy tissues, with multiple genes (e.g. CCL13, CCL19, CCR7 and TLR4) significantly increased in aged animals. Additionally, gene expression patterns for M1 macrophages were significantly increased in adult health versus periodontitis and aged healthy versus periodontitis. The findings supported a significant increase in macrophages with aging and in periodontitis. The primary increases in both healthy aged tissues and, particularly periodontitis tissues appeared in the M1 phenotype

Preservation of Axillary Lymph Nodes Compared with Complete Dissection in T1–2 Breast Cancer Patients Presenting One or Two Metastatic Sentinel Lymph Nodes: The SINODAR-ONE Multicenter Randomized Clinical Trial

Background: The SINODAR-ONE trial is a prospective noninferiority multicenter randomized study aimed at assessing the role of axillary lymph node dissection (ALND) in patients undergoing either breast-conserving surgery or mastectomy for T1–2 breast cancer (BC) and presenting one or two macrometastatic sentinel lymph nodes (SLNs). The endpoints were to evaluate whether SLN biopsy (SLNB) only was associated with worsening of the prognosis compared with ALND in terms of overall survival (OS) and relapse. Methods: Patients were randomly assigned (1:1 ratio) to either removal of ≥ 10 axillary level I/II non-SLNs followed by adjuvant therapy (standard arm) or no further axillary treatment (experimental arm). Results: The trial started in April 2015 and ceased in April 2020, involving 889 patients. Median follow-up was 34.0 months. There were eight deaths (ALND, 4; SNLB only, 4), with 5-year cumulative mortality of 5.8% and 2.1% in the standard and experimental arm, respectively (p = 0.984). There were 26 recurrences (ALND 11; SNLB only, 15), with 5-year cumulative incidence of recurrence of 6.9% and 3.3% in the standard and experimental arm, respectively (p = 0.444). Only one axillary lymph node recurrence was observed in each arm. The 5-year OS rates were 98.9% and 98.8%, in the ALND and SNLB-only arm, respectively (p = 0.936). Conclusions: The 3-year survival and relapse rates of T1–2 BC patients with one or two macrometastatic SLNs treated with SLNB only, and adjuvant therapy, were not inferior to those of patients treated with ALND. These results do not support the use of routine ALND

Assessing the quality of models of the ambient solar wind

In this paper we present an assessment of the status of models of the global Solar Wind in the inner heliosphere. We limit our discussion to the class of models designed to provide solar wind forecasts, excluding those designed for the purpose of testing physical processes in idealized configurations. In addition, we limit our discussion to modeling of the ‘ambient’ wind in the absence of coronal mass ejections. In this assessment we cover use of the models both in forecast mode and as tools for scientific research. We present a brief history of the development of these models, discussing the range of physical approximations in use. We discuss the limitations of the data inputs available to these models and its impact on their quality. We also discuss current model development trends